---
language: en
license: mit
library_name: transformers
pipeline_tag: token-classification
tags:
  - deberta
  - ner
  - token-classification
  - cybersecurity
  - logs
base_model: microsoft/deberta-v3-base
---

# DeBERTa-v3 Log Entity Recognition Model

Fine-tuned DeBERTa-v3-small for Named Entity Recognition on system and cloud logs.

## Model Details
- **Base Model**: microsoft/deberta-v3-base
- **Training Data**: 7003 synthetic + real logs
- **Validation F1**: Check evaluation_results.txt

## Entities
['O', 'B-SERVICE', 'I-SERVICE', 'B-ERROR', 'I-ERROR', 'B-HOST', 'I-HOST', 'B-PROCESS', 'I-PROCESS']

## Usage
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
from peft import PeftModel

model_id = "YOUR_USERNAME/log-ner-deberta-lora"

tokenizer = AutoTokenizer.from_pretrained(model_id)
base_model = AutoModelForTokenClassification.from_pretrained("microsoft/deberta-v3-base")
model = PeftModel.from_pretrained(base_model, model_id)

# Extract entities
text = "nginx timeout on server1"
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)
```

## Training Configuration
- LoRA rank: 32
- Training epochs: 15
- Learning rate: 0.0003